Impute Missing Glucose Values in CGM Data R package CGMissingDataR version 0.0.2 Imputes missing glucose values in repeated-measures continuous glucose monitoring (CGM) data. Workflows create time-series features from raw timestamps, support model selection, and return the user's original columns plus an imputed glucose column. Methods include multiple imputation by chained equations (MICE; Azur et al. (2011) <doi:10.1002/mpr.329>), Random Forest regression (Breiman (2001) <doi:10.1023/A:1010933404324>), k-nearest-neighbor regression (Zhang (2016) <doi:10.21037/atm.2016.03.37>), XGBoost (Chen and Guestrin (2016) <doi:10.1145/2939672.2939785>), LightGBM (Ke et al. (2017) <https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision>), and ARIMA forecasting with the forecast framework (Hyndman and Khandakar (2008) <doi:10.18637/jss.v027.i03>). A Python-compatible backend uses 'reticulate' to call 'pandas', 'scikit-learn', 'statsmodels', Python 'xgboost', and optional Python 'lightgbm'. Software Depends: R (>= 4.3) Imports: mice, FNN, ranger, data.table, xgboost, lightgbm, forecast, CGManalyzer, lifecycle, reticulate, shiny Suggests: testthat (>= 3.0.0), spelling, knitr, rmarkdown Shubh Saraswat <shubh.saraswat00@gmail.com> Comprehensive R Archive Network (CRAN) Shubh Saraswat [cre, aut, cph] (ORCID: <https://orcid.org/0009-0009-2359-1484>), Hasin Shahed Shad [aut], Xiaohua Douglas Zhang [aut] (ORCID: <https://orcid.org/0000-0002-2486-7931>) GPL (>= 2) 2026-05-30 application/tgz https://CRAN.R-project.org/package=CGMissingDataR doi:10.32614/CRAN.package.CGMissingDataR en-US